Equity · Global · ETFs, stocks · Monthly Free

Combining Smart Factors Momentum and Market Portfolio

Academic paper

The Active vs Passive: Smart Factors, Market Portfolio or Both?

AuthorsMatus Padysak

Institute

Comenius University Bratislava
Comenius University - Faculty of Mathematics, Physics and Informatics
Quantpedia.com

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3745517

Strategy in a nutshell

The investment universe consists of factors from the Alpha Architect’s Factor Investing Data Library (factor for all major investment styles such as Value, Quality, Momentum, Size and Volatility) based on the top 1500 US stocks. Firstly construct the fast and slow signals for each factor. The fast signal is the past one-month return, and the slow signal is the past twelve-months return. For each type of signal, to obtain the weights, cross-sectionally rank signals’ based on their absolute values. The weight for the individual slow or fast signal is equal to the corresponding rank divided by the sum of all ranks and multiplied by the signal’s sign (equations 3 and 4 in the paper). For the dynamically blended strategy (smart factors strategy), each factor has a final weight of three-quarters of the weight of fast signal plus one-quarter of the weight of slow signal (equation 12). Nextly, consider the top 1500 US stocks as the market portfolio. The combined smart factors and market strategy finds the weights of the market and factor portfolio using past moving averages of the returns. The combined strategy looks back on the past twelve months, and twelve MAs of the returns. Suppose the MA for active investing (factor momentum) is larger than MA for market portfolio, then the active investing scores one point. Otherwise, the market portfolio gets one point. Therefore, each month, the weight of the factor momentum and market portfolio is determined by the number of “winning” (loosing) moving averages (equations 13 and 14). The strategy is rebalanced monthly.

Economic rationale

Firstly, the functionality of factor strategies was proven by numerous academic researches. The same could be said about the momentum in factors since both the time-series and cross-sectional momentum strategies are well-examined and proved to be functional. The factor momentum also solves the problem of underperforming factors because of the wrong portfolio sort (for example, when growth outperforms value or big size outperforms small size).

The blending of the factor seems to be important because the slow signals tend to be unreactive to changes in trend, and fast signals are often false alarms. Therefore, the weight of the factors should be adjusted based on signal interreactions. Lastly, the dynamical weights based on the strength of the signals is also a widely utilized approach that was found to be effective also in the factor universe.

Although the active factor strategy largely outperforms naive equal-weighting of the factors or signals alone, it would have been largely beaten by the market. However, the active factor strategy and market are negatively correlated. This correlation is statistically significant using a robust non-parametric test, and this result suggests that the two portfolios could be combined to achieve the best of the two approaches. The backtest confirms this theory, since the combined strategy using moving averages, has the largest return, the lowest volatility or drawdown, and the returns distribution is much more favourable.

Entry & exit

Entry

On each month, scan IWM, BIL. Long every name whose close is above its 200-day SMA, equal-weight. Skip names still below the average. An empty weight dict is 100% cash.

Exit

At the next rebalance, drop any name whose close has fallen back to or below the 200-day SMA. If the whole book is below trend, go fully to cash. Fills use 5 bps commission and 2 bps slippage.

Market data required

Needs at least 200 adjusted daily closes on IWM, BIL before the first signal is ready. ETF columns print on exchange sessions only. The lab loads Yahoo Finance via backtest.data.load_daily_prices.

Symbols

IWM, BIL

Frequency

Monthly · Daily adjusted close; signals rebalance on the month change (last available session)

History needed

≥ 200 trading days per symbol (about 10 months), plus a few extra bars so the first signal can fire.

Fields

Adjusted close (Yahoo auto_adjust)
Session date index

Credentials

None for paper and the onsite lab
Broker or exchange API keys only for live orders

APIs & providers

Quant Buffet Data API

The lab calls load_daily_prices for the ASSETS panel of adjusted closes, then caches CSV so backtests replay the same series.

Provider: backtest.data.load_daily_prices
Auth: None for paper / sandbox backtests
Endpoints: load_daily_prices(ASSETS, start="2000-01-01")

API / docs → /en/docs/data-api

Yahoo Finance via yfinance

US equities and ETFs, adjusted OHLCV, free for research (rate limits apply).

Provider: yfinance → Yahoo Finance
Auth: None for public history
Endpoints: Ticker.history / yf.download(auto_adjust=True)

API / docs → github.com/ranaroussi/yfinance

Onsite backtest IDE

Quant Buffet native backtest IDE

Edit and run Quant Buffet Python for Combining Smart Factors Momentum and Market Portfolio in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →

IDE · 40 lines

"""
Quant Buffet interactive lab — combining-smart-factors-momentum-and-market-portfolio
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS = ['IWM', 'BIL']
SMA_DAYS = 200

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    sma = prices[cols].rolling(SMA_DAYS, min_periods=SMA_DAYS).mean()
    state = {"last": None}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        if sma.loc[dt].isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        long = [
            s for s in cols
            if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
            and prices.at[dt, s] > sma.at[dt, s]
        ]
        w = {} if not long else {s: 1.0 / len(long) for s in long}
        engine.set_target_weights(dt, w)

    ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
    return on_day, ready

"""
Quant Buffet interactive lab — combining-smart-factors-momentum-and-market-portfolio
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS = ['IWM', 'BIL']
SMA_DAYS = 200

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    sma = prices[cols].rolling(SMA_DAYS, min_periods=SMA_DAYS).mean()
    state = {"last": None}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        if sma.loc[dt].isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        long = [
            s for s in cols
            if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
            and prices.at[dt, s] > sma.at[dt, s]
        ]
        w = {} if not long else {s: 1.0 / len(long) for s in long}
        engine.set_target_weights(dt, w)

ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
    return on_day, ready

Quant Buffet syntax cheat sheet (copy / insert)

Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.

Required imports

Only these libraries are allowed in the sandbox.

from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS list (whitelisted ETFs)

Module-level list. Tickers must be in the Quant Buffet whitelist.

ASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]

make_on_day contract

Must return (on_day, ready). on_day calls engine.set_target_weights.

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    sma = prices[cols].rolling(200, min_periods=200).mean()
    state = {"last": None}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        if sma.loc[dt].isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        long = [
            s for s in cols
            if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
            and prices.at[dt, s] > sma.at[dt, s]
        ]
        weights = {} if not long else {s: 1.0 / len(long) for s in long}
        engine.set_target_weights(dt, weights)

    ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
    return on_day, ready

Set target weights

Weights should sum to about 1.0. Empty dict = 100% cash.

engine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})

Live backtest performance

CAGR

3.82%

Sharpe

0.36

Max DD

-23.91%

Vol

12.55%

Sortino

0.55

Beta

0.32

Up days

58%

Run the backtest to populate charts.

Order records

Run the backtest to load every order fill alongside performance.

Export to your platform

Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.

Target platform

Run in: QuantConnect Cloud or LEAN CLI · QCAlgorithm with Equity securities and monthly rebalance.

Detected pattern: SMA trendAssets: IWM, BIL

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Combining Smart Factors Momentum and Market Portfolio
# Detected pattern: SMA trend
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.

from AlgorithmImports import *


class QuantBuffetExport(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2010, 1, 1)
        self.SetCash(100000)
        tickers = ["IWM", "BIL"]
        self.symbols = []
        for t in tickers:
            if "-" in t:  # crypto proxy e.g. BTC-USD
                self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
            else:
                self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
        self.Schedule.On(
            self.DateRules.MonthStart(self.symbols[0]),
            self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
            self.Rebalance,
        )
        # Logic: Long assets where close > SMA(200); equal-weight; monthly.

    def Rebalance(self):
        longs = []
        for symbol in self.symbols:
            hist = self.History(symbol, 200 + 5, Resolution.Daily)
            if hist.empty: continue
            close = hist["close"].unstack(level=0).iloc[:, 0] if hasattr(hist["close"], "unstack") else hist["close"]
            if len(close) < 200: continue
            if float(close.iloc[-1]) > float(close.iloc[-200:].mean()):
                longs.append(symbol)
        weight = 1.0 / len(longs) if longs else 0.0
        for symbol in self.symbols:
            self.SetHoldings(symbol, weight if symbol in longs else 0.0)

Exported code uses the platform’s native classes and libraries. Install dependencies in your third-party IDE, then run. Validate before live trading.